Computational Modeling of the Interplay between External Signaling and Transcription Rewiring using Spatial Transcriptomics and Single Cell Multiome Data

Cell-cell interactions (CCI) play crucial roles in nearly all important biological processes, including cell differentiation, inflammation, wound repair and oncogenesis. CCI typically occurs when a sender cell's ligand interacts with a receiver cell's receptor, leading to changes in the target cell's transcription factor (TF) activities. Despite advances in high-throughput methods, there is still a gap in the identification of CCIs from these data, with current approaches relying largely on low-throughput manual methods. To address this gap, the proposed research aims to develop computational frameworks to model the impact of external signals on a cell's internal states using a combination of single-cell multiome (scMultiome) and spatial transcriptomics (ST) data. The PI plans to create a series of computational methods based on his prior work, including: 1) an improved version of BayesPrism, to more accurately deconvolve cell type fraction and cell type-specific gene expression from bulk RNA-seq and ST; 2) a novel computational method for jointly modeling the relationship between CCI and downstream gene expression using ST and providing interpretable biological insights about CCI; 3) an extension of this framework to incorporate epigenetic information measured from single-cell multiomics data. These tools will address the limitations in existing methods including: 1) lack of robustness when using multiple scRNA-seq references for statistical deconvolution; 2) the reliance on the transcription levels of receptor/ligand to impute CCI, which may not reflect active protein levels and may fail to account for ineffective interactions due to physical separation or epigenetic states; and 3) reliance on incomplete motif information to infer TF activity and the inefficiency of existing model’s in capturing the complex relationship between DNA sequence and TF activity. The proposed methods will be applied to study the impact of CCI in multiple biological systems enriched for CCI, including inflammation and tissue senescence. By providing a generalizable tool for understanding CCI, this work aims to fill the current gap in computational methodologies and advance the understanding of misregulation of CCI in various disease contexts, with the goal of developing new therapeutic targets. Project Number: 4R00HG013429-03 | Fiscal Year: 2026 | NIH Institute/Center: National Human Genome Research Institute (NHGRI) | Principal Investigator: Tin Yi Chu | Institution: UNIVERSITY OF VIRGINIA, CHARLOTTESVILLE, VA | Award Amount: $248,999 | Activity Code: R00 | Study Section: NSS View on NIH RePORTER: https://reporter.nih.gov/project-details/11474018